The article provides evidence on the effect of local public governance on the impact of public investment on local and regional economic growth, using spatial and regional logic. The research uses the spatial Durbin model and produces a panel data set that was conducted on 63 provinces of Vietnam from 2006 to 2022. Based on the interaction between public governance and public investment, the main findings indicate that their interaction plays an important role in adjusting the effects of public investment and public governance on economic growth not only in the locality but also spillover to neighboring localities in both the short and long terms. It suggests that local public governance not only hampers the impact of local public investment on local economic growth but also has spillover effects on the growth of neighboring provinces or regions in Vietnam. Additionally, the results of detailed analysis of PCI component indicators show that many aspects of local public governance are hindering local economic growth but contributing to promoting neighboring localities economic growth. Or, it has no effect the locality but promote or hinder the regional economic growth. The findings in this study implies that authorities of localities need to be cautious when using resources to improve the various aspects of public governance when designing strategies to enhance the quality of local public governance. It also suggests that this spillover effect is a crucial factor in advocating for more redistributive fiscal policies and regional governance policies aimed at reducing economic disparities caused by territorial boundaries. Therefore, authorities should prioritize regional cooperation strategies in their decisions regarding public governance and public capital allocation.

Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.

The increase in world carbon emissions is always in line with national economic growth programs, which create negative environmental externalities. To understand the effectiveness of related factors in mitigating CO₂ emissions, this study investigates the intricate relationship among macro-pillars such as economic growth, foreign investment, trade and finance, energy, and renewable energy with CO₂ emissions of the high gross domestic product economies in East Asia Pacific, such as China, Japan, Korea, Australia and Indonesia (EAP-5). Through the application of the Vector Error Correction Model (VECM), this research reveals the long-term equilibrium and short-term dynamics between CO₂ emissions and selected factors from 1991 to 2020. The long-term cointegration vector test results show that economic growth and foreign investment contribute to carbon reduction. Meanwhile, the short-term Granger causality test shows that economic growth has a two-way causality towards carbon emissions, while energy consumption and renewable energy consumption have a one-way causality towards carbon emissions. In contrast, the variables trade, foreign direct investment, and domestic credit to the private sector do not have two-way causality towards CO₂ emissions. The findings reveal that economic growth and foreign investment play significant roles in carbon reduction, which are observed in long-term causality relationships, while energy consumption and renewable energy are notable factors. Thus, the study offers implications for mitigating environmental concerns on national economic growth agendas by scrutinizing and examining the efficacy of related factors.

A The meaning of life is the purpose that defines a person’s existence based on a set of fundamental objectives that give meaning to life or not. Furthermore, not all individuals have a meaning in life, and it may be absent at some point or stage of life. Objective: To analyze Peruvian older adults’ socioeconomic factors and the meaning of life. Method: A descriptive, comparative, quantitative cross-sectional study was conducted. One thousand older adults were intentionally selected through quotas of 100 older adults in 10 localities in Arequipa, Peru. They were administered a survey validated with high levels of reliability on the meaning of life and socioeconomic factors. Results: A moderate level of meaning in life was found. Most older adults believe that increasing age decreases the purpose of living, and existential emptiness grows. Conclusions: Statistically significant differences (p < 0.05) were found between the meaning of life and the following socioeconomic factors: retirement, religion, educational level, cohabitation, marital status, income, and occupation. It is understood that older adults who scored higher on these factors indicate having meaning in life because they still fulfill the role of providers for the family economy, being util to their families compared to the majority who scored low, which indicates an absence of meaning of life leading to an increase existential void.

Remote sensing technologies have revolutionized forestry analysis by providing valuable information about forest ecosystems on a large scale. This review article explores the latest advancements in remote sensing tools that leverage optical, thermal, RADAR, and LiDAR data, along with state-of-the-art methods of data processing and analysis. We investigate how these tools, combined with artificial intelligence (AI) techniques and cloud-computing facilities, enhance the analytical outreach and offer new insights in the fields of remote sensing and forestry disciplines. The article aims to provide a comprehensive overview of these advancements, discuss their potential applications, and highlight the challenges and future directions. Through this examination, we demonstrate the immense potential of integrating remote sensing and AI to revolutionize forest management and conservation practices.